Use Cases that Involve Public Safety

In addition to the recommendations listed in Best practices for Sensors, Input Images and Videos and
Guidance for using IndexFaces, you should use the following best practices when deploying face detection and recognition
systems
in use cases that involve public safety. First,
customers should use confidence thresholds of 99% or higher to reduce errors and false
positives. Second, you should involve human
reviewers to verify results received from a face detection or recognition system,
and you should not make decisions based on system output without
additional human review. Face detection and recognition systems should serve as a
tool to help narrow the field and allow humans to expeditiously
review and consider options. Third, we recommend that you should be transparent about
the use of face detection and recognition systems
in these use cases, including, wherever possible, informing end users and subjects
about the use of these systems, obtaining consent for such use,
and providing a mechanism where end users and subjects can provide feedback to improve
the system.

If you are planning to use a face detection or face recognition system for use cases
that involve public safety you should
employ the best practices mentioned previously. In addition, you should consult published
resources on the use of face recognition. This includes the
Face Recognition Policy Development Template For Use In Criminal Intelligence and
Investigative Activities
provided by the Bureau of Justice Assistance of the Department of Justice.
The template provides several facial recognition and biometric-related resources and
is designed to provide law enforcement and public safety agencies with a framework
for
developing face recognition policies that comply with applicable laws, reduce privacy
risks, and establish entity accountability and oversight. Additional resources
include
Best Privacy Practices for Commercial Use of Facial Recognition by the National Telecommunications and Information Administration and
Best Practices for Common Uses of Facial Recognition
by the staff of the Federal Trade Commission. Other resources may be developed and
published in the future, and you should continuously educate yourself on this important
topic.

As a reminder, you must comply with all applicable laws in their use of AWS services,
and you may not use any AWS service in a manner that violates the rights of others
or may be harmful to others.
This means that you may not use AWS services for public safety use cases in a way
that illegally discriminates against a person or violates a person’s due process,
privacy,
or civil liberties. You should obtain appropriate legal advice as necessary to review
any legal requirements or questions regarding your use case.

Using Amazon Rekognition to Help Public Safety

Amazon Rekognition can help in public safety and law enforcement scenarios—such as
finding lost children, combating human
trafficking, or preventing crimes. In public safety and law enforcement scenarios,
consider the following:

Use Amazon Rekognition as the first step in recognizing an individual. The responses
from Amazon Rekognition
face operations allow you to quickly get a set of potential matches for further consideration.

Don’t use Amazon Rekognition responses to make autonomous decisions for scenarios
that require analysis by a
human. An example of this is determining who committed a crime. Instead, have a human
review the responses, and use that
information to inform further decisions.

Use a 99% similarity threshold for scenarios where highly accurate face similarity
matches are necessary.
An example of this is authenticating access to a building.

Use a similarity threshold lower than 99% for scenarios that benefit from a larger
set of potential matches.
An example of this is finding missing persons. If necessary, you can use the Similarity
response attribute to determine
how similar potential matches are to the person you want to recognize.

Have a plan for false-positive face matches that are returned by Amazon Rekognition.
For example,
improve matching by using multiple images of the same person when you build the index
with the
IndexFaces operation. For more information, see
Guidance for using IndexFaces.

In other use cases (such as social media), we recommend you use your best judgement
to assess if the Amazon Rekognition
results need human review. Also, depending on your application’s requirements, the
similarity threshold can be lower.

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